The global network of people and machines includes millions of users of the Internet and individuals with a mobile phone. Many people have a social media profile and interact with social media several times each day. This form of interconnectedness has not only opened new ways of relating with one another—and with things—but it also enables the identification of these relationships. Individuals connected via social media have a defined connection that is memorialized in data.
Beyond identifying traditional relationships, the shift toward individuals generating more and more data each day—through social media, e-commerce, remote workplaces, GPS location services, and distance learning, to name but a few—has made available a wealth of data that describes our relationships. Prior efforts to identify and quantify these relationships have been met with limited success for several reasons, one of which is the sheer volume of data available and lack of an intelligent method for processing this data.
Digital interconnectedness is only one part of the larger picture. People continue to interact face-to-face and maintain personal and professional relationships with varying degrees of closeness. Existing systems that have examined connections have largely omitted these real world relationships and instead examined only online networks such as social media.
While the amount of available computing and data processing has increased dramatically in recent years, more than raw computing power is required to intelligently identify, quantify, and traverse diverse networks of connections.